---
base_model: Fsoft-AIC/videberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videberta-base_1024
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# videberta-base_1024

This model is a fine-tuned version of [Fsoft-AIC/videberta-base](https://huggingface.co/Fsoft-AIC/videberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5931
- Accuracy: 0.75

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.18
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5982        | 0.1   | 50   | 0.6297          | 0.75     |
| 0.5505        | 0.21  | 100  | 0.5696          | 0.75     |
| 0.5838        | 0.31  | 150  | 0.5629          | 0.75     |
| 0.5925        | 0.41  | 200  | 0.5931          | 0.75     |
| 0.7003        | 0.52  | 250  | 0.5931          | 0.75     |
| 0.606         | 0.62  | 300  | 0.5931          | 0.75     |
| 0.6744        | 0.72  | 350  | 0.5931          | 0.75     |
| 0.6448        | 0.83  | 400  | 0.5931          | 0.75     |
| 0.7365        | 0.93  | 450  | 0.5931          | 0.75     |
| 0.6083        | 1.03  | 500  | 0.5931          | 0.75     |
| 0.6217        | 1.14  | 550  | 0.5931          | 0.75     |
| 0.642         | 1.24  | 600  | 0.5931          | 0.75     |
| 0.6433        | 1.34  | 650  | 0.5931          | 0.75     |
| 0.7497        | 1.45  | 700  | 0.5931          | 0.75     |
| 0.6385        | 1.55  | 750  | 0.5931          | 0.75     |
| 0.6581        | 1.65  | 800  | 0.5931          | 0.75     |
| 0.6201        | 1.76  | 850  | 0.5931          | 0.75     |
| 0.6424        | 1.86  | 900  | 0.5931          | 0.75     |
| 0.619         | 1.96  | 950  | 0.5931          | 0.75     |
| 0.6807        | 2.07  | 1000 | 0.5931          | 0.75     |


### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1